Many medical diagnoses rely on non-invasive diagnostic tools to provide information, often in the form of images, descriptive of status of internal portions or organs of a patient. These tools include thermal imaging (e.g., mammography), ultrasonic probes, magnetic resonance imaging techniques, positron emission tomography, computed tomography (CT), single photon emission-computed tomography (SPECT) and optical imaging and/or X-ray-based techniques. In some minimally invasive instances, imaging aids, such as contrast-enhancing agents, are introduced into the subject or patient to aid in increasing available data content from the non-destructive imaging technique or techniques being employed.
Each of these tools presents advantages in particularized situations, has technological limitations, may require set-up and analysis time, can include risks and also has associated costs. As a result, a cost-benefit analysis that also reflects the degree of urgency with respect to a particular diagnostic trajectory often favors usage of X-ray-based measurement techniques.
Several factors influence image quality resulting from an X-ray procedure. Statistical photon noise resulting from characteristics of the X-ray source and the X-ray generation conditions tends to dominate other noise sources in formation of an X-ray-based image. Signal conditioning consistent with achieving suitable contrast between various image portions, and contrast enhancement techniques, are also important considerations in providing diagnostic images, and these issues require increasingly sophisticated treatment as dose and/or photon energy are decreased.
One of the key tenets of medical X-ray imaging is that image quality should be carefully considered in determining exposure conditions. Exposure considerations include predetermined dose criteria vis-á-vis the dose of X-ray delivered to the test subject or patient in order to provide images. The design and operation of a detector used for medical X-ray imaging should therefore be tailored, responsive to the particularized task and measurement conditions, including variables in test subject mass, opacity and the like, to provide high image quality for each X-ray exposure that is incident on the detector.
However, diagnostic medical tools such as X-ray-based imaging systems are precision instruments, very carefully designed, and then built to exacting standards. As such, these kinds of imaging systems represent significant capital investments. Additionally, training personnel to maintain and calibrate such equipment, to operate the equipment, and then to interpret data obtained via these diagnostic tools, also encompasses additional investment. Further, comparison of data from one assessment to another, and from one timeframe to another, is greatly facilitated when the data are collected and processed in a relatively well-understood and documented context. At the same time, technical developments may provide opportunity to leverage existing infrastructural elements by retrofitting them using sophisticated, newly-developed technological subsystems, and this also may facilitate capabilities not present in the ensemble of system elements contemplated at initial design and deployment.
For example, X-ray systems and other non-destructive and largely non-invasive characterization devices have realized dramatic changes in capability during the last century or more. Medical diagnostic capabilities unimaginable prior to C. W. Roentgen's observations of X-ray-based images in 1895 have fostered intense and remarkably fruitful research, study and development, improving medical treatment capabilities to such an extent as to have, in turn, played pivotal roles leading to conception and subsequent maturation of entirely new medical specialties and treatment options.
One new tool resulting from this research employs pixelated X-ray detectors (detectors comprising a geometric array of multiple detector elements, where each detector element may be individually representative of at least a portion of a picture element or pixel in the resultant image). These detectors are increasingly being employed, particularly for medical imaging. Among other things, they facilitate digital representation of images and other data resulting from usage of the systems, which, in turn, enables digital signal processing, data storage and data transmission technologies.
A significant result of these technological innovations is that the potential and capability for real-time consultation between multiple experts, such as medical doctors, during what is called the “golden hour” following a medically-significant event, is greatly enhanced. Representation of such information in digital formats eases transmission, reception and standardized display of the information without incurring loss of acuity of data obtained from the measurement process and greatly eases reduction of noise from the transmission/reception process. In turn, this facilitates capability for multiple experts to collaborate virtually instantly, even from geographically diverse locations, despite extreme scenarios, e.g., triage following an unanticipated disaster. As a result, these capabilities represent strong impetus to incorporate new capabilities within existing diagnostic instruments.
Another significant advance in X-ray imaging involves computerized tomography (CT). The term “tomography” is formed via conjunction of tomos (Greek, “section”), and graphia (“describing”). CT techniques rely on collection of a series of pixelated sectional views, or slices, each taken at a slightly different angle vis-à-vis the test subject, usually in conformance with a helical scanning protocol. The resulting digitized data permits a number of different graphical representations of structures or organs within the test subject in conformance with three-dimensional viewing and analysis, and other techniques.
As these new imaging tools have been developed and combined to provide synergistic results, the volume of data resulting from an imaging procedure has grown along with the increasing gamut of capabilities for analyzing, displaying and employing the data. As a result, it is increasingly difficult and time-consuming to examine the many elements of information resulting from an imaging procedure in order to determine and select the vital few elements needed for various highly specialized types of procedures. In turn, this explosion of data results in delay in applying the results from the procedure, and this is particularly felt in situations requiring extremely rapid response to unexpected demand for medical services, such as an influx of many critically-injured patients following one or more traumatic events such as vehicular disasters and the like.
An additional aspect that tends to compound the acuteness of these issues results from the high investment required in order to provide the imaging machines and to engage appropriately-trained staff and physicians in conjunction with that imaging capability as well as the ancillary medical equipment and infrastructure. A consequence of such is that time and effort saved in the course of pre-planned but complex procedures, such as detection and correction of aneurisms and the like, tends to increase the availability of the equipment and/or staff when such unanticipated events result in abrupt, and extremely time-critical, demand for those capabilities.
For the reasons stated above, and for other reasons discussed below, which will become apparent to those skilled in the art upon reading and understanding the present disclosure, there are needs in the art to provide more highly automated image computation engines and protocols for application and usage of such capabilities, in order to streamline gathering of information in support of increasingly stringent and exacting performance and economic standards in settings such as medical instrumentation.